o
    Zh                     @   s0  d dl mZ d dlmZmZmZmZmZ d dlm	Z	 d dl
Z
d dlm  mZ d dl
mZ d dlmZ d dlmZ dZg d	Zd
d Zdd Zdd Zd(ddZdd Zd(ddZe	dddZe	dddZG dd deeef ZG dd deeef eZ d)d ed!e!d"efd#d$Z"d)d%ed!e!d"efd&d'Z#dS )*    )update_wrapper)AnyCallableGenericoverloadUnion)TypeVarN)Tensoris_tensor_like)_Numbergox?)broadcast_alllogits_to_probsclamp_probsprobs_to_logitslazy_propertytril_matrix_to_vecvec_to_tril_matrixc                     s   t dd | D stdt dd | D s@tt d | D ]}t|tjr1t|j|jd  nq fdd| D }tj	| S tj	|  S )	a  
    Given a list of values (possibly containing numbers), returns a list where each
    value is broadcasted based on the following rules:
      - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`.
      - Number instances (scalars) are upcast to tensors having
        the same size and type as the first tensor passed to `values`.  If all the
        values are scalars, then they are upcasted to scalar Tensors.

    Args:
        values (list of `Number`, `torch.*Tensor` or objects implementing __torch_function__)

    Raises:
        ValueError: if any of the values is not a `Number` instance,
            a `torch.*Tensor` instance, or an instance implementing __torch_function__
    c                 s   s"    | ]}t |pt|tV  qd S N)r   
isinstancer   .0v r   H/var/www/auris/lib/python3.10/site-packages/torch/distributions/utils.py	<genexpr>*   s     z broadcast_all.<locals>.<genexpr>ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c                 s   s    | ]}t |V  qd S r   r
   r   r   r   r   r   /   s    )dtyper   devicec                    s*   g | ]}t |r
|ntj|fi  qS r   )r   torchZtensorr   optionsr   r   
<listcomp>5   s    z!broadcast_all.<locals>.<listcomp>)
all
ValueErrordictr   Zget_default_dtyper   r	   r   r   Zbroadcast_tensors)valuesvalue
new_valuesr   r    r   r      s    


r   c                 C   sB   t j rt t j| ||dt j| ||dS t j| ||d S )Nr   )r   _C_get_tracing_statenormalZzerosZonesemptyZnormal_)shaper   r   r   r   r   _standard_normal<   s   
r.   c                 C   s0   |dkr| S | j d|  d }| |dS )z
    Sum out ``dim`` many rightmost dimensions of a given tensor.

    Args:
        value (Tensor): A tensor of ``.dim()`` at least ``dim``.
        dim (int): The number of rightmost dims to sum out.
    r   N)r/   )r-   Zreshapesum)r'   dimZrequired_shaper   r   r   _sum_rightmostF   s   r2   Fc                 C   s   |rt | S tj| ddS )a  
    Converts a tensor of logits into probabilities. Note that for the
    binary case, each value denotes log odds, whereas for the
    multi-dimensional case, the values along the last dimension denote
    the log probabilities (possibly unnormalized) of the events.
    r/   )r1   )r   ZsigmoidFZsoftmax)Zlogits	is_binaryr   r   r   r   T   s   
r   c                 C   s    t | jj}| j|d| dS )a  Clamps the probabilities to be in the open interval `(0, 1)`.

    The probabilities would be clamped between `eps` and `1 - eps`,
    and `eps` would be the smallest representable positive number for the input data type.

    Args:
        probs (Tensor): A tensor of probabilities.

    Returns:
        Tensor: The clamped probabilities.

    Examples:
        >>> probs = torch.tensor([0.0, 0.5, 1.0])
        >>> clamp_probs(probs)
        tensor([1.1921e-07, 5.0000e-01, 1.0000e+00])

        >>> probs = torch.tensor([0.0, 0.5, 1.0], dtype=torch.float64)
        >>> clamp_probs(probs)
        tensor([2.2204e-16, 5.0000e-01, 1.0000e+00], dtype=torch.float64)

       )minmax)r   finfor   epsclamp)probsr9   r   r   r   r   `   s   r   c                 C   s,   t | }|rt|t|  S t|S )a$  
    Converts a tensor of probabilities into logits. For the binary case,
    this denotes the probability of occurrence of the event indexed by `1`.
    For the multi-dimensional case, the values along the last dimension
    denote the probabilities of occurrence of each of the events.
    )r   r   loglog1p)r;   r4   Z
ps_clampedr   r   r   r   z   s   
r   TT)contravariantR)	covariantc                   @   s   e Zd ZdZdeegef ddfddZe	dddde	dd	fd
dZ
eddede	defddZ
	ddeedf de	ddfddZ
dS )r   z
    Used as a decorator for lazy loading of class attributes. This uses a
    non-data descriptor that calls the wrapped method to compute the property on
    first call; thereafter replacing the wrapped method into an instance
    attribute.
    wrappedreturnNc                 C   s   || _ t| | d S r   )rB   r   selfrB   r   r   r   __init__   s   zlazy_property.__init__instanceobj_typez!_lazy_property_and_property[T, R]c                 C      d S r   r   rE   rG   rH   r   r   r   __get__   s   zlazy_property.__get__c                 C   rI   r   r   rJ   r   r   r   rK      s   z%R | _lazy_property_and_property[T, R]c                 C   sX   |d u r	t | jS t  | |}W d    n1 sw   Y  t|| jj| |S r   )_lazy_property_and_propertyrB   r   Zenable_gradsetattr__name__)rE   rG   rH   r'   r   r   r   rK      s   

r   )rN   
__module____qualname____doc__r   r>   r@   rF   r   r   rK   r   r   r   r   r   r      s,    
r   c                   @   s,   e Zd ZdZdeegef ddfddZdS )rL   zWe want lazy properties to look like multiple things.

    * property when Sphinx autodoc looks
    * lazy_property when Distribution validate_args looks
    rB   rC   Nc                 C   s   t | | d S r   )propertyrF   rD   r   r   r   rF      s   z$_lazy_property_and_property.__init__)rN   rO   rP   rQ   r   r>   r@   rF   r   r   r   r   rL      s     rL   matdiagrC   c                 C   s   | j d }tj s$|| k s||kr$td| d|  d|d  dtj|| jd}||dd|d  k }| d|f }|S )	z
    Convert a `D x D` matrix or a batch of matrices into a (batched) vector
    which comprises of lower triangular elements from the matrix in row order.
    r/   zdiag (z) provided is outside [z, r5   z].r   .)r-   r   r)   r*   r$   aranger   view)rS   rT   nrV   	tril_maskvecr   r   r   r      s   
"r   rZ   c                 C   s  dd|   dd|  d d| j d   dt| |d   d  d }t| jj}tj sEt|| |krEt	d| j d  dd	 t
|tjrQt| nt|}| | j d
d t||f }tj|| jd}||dd|d  k }| |d|f< |S )z
    Convert a vector or a batch of vectors into a batched `D x D`
    lower triangular matrix containing elements from the vector in row order.
    r5         r/      g      ?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.NrU   .)r-   absr   r8   r   r9   r)   r*   roundr$   r   r	   itemZ	new_zerosSizerV   r   rW   )rZ   rT   rX   r9   rS   rV   rY   r   r   r   r      s$   4 "r   )F)r   )$	functoolsr   typingr   r   r   r   r   Ztyping_extensionsr   r   Ztorch.nn.functionalnnZ
functionalr3   r	   Ztorch.overridesr   Ztorch.typesr   Zeuler_constant__all__r   r.   r2   r   r   r   r>   r@   r   rR   rL   intr   r   r   r   r   r   <module>   s,   "


